Skip to main content

Laplace-Dirichlet Rule-based algorithm for assigning myocardial fiber orientations.

Project description

_

Create and publish a Docker image pre-commit Test package PyPI version GitHub License Ruff

Laplace-Dirichlet Rule-Based (LDRB) algorithm for assigning myocardial fiber orientations

A software for assigning myocardial fiber orientations based on the Laplace Dirichlet Ruled-Based algorithm based on FEniCSx.

Bayer, J.D., Blake, R.C., Plank, G. and Trayanova, N.A., 2012. A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Annals of biomedical engineering, 40(10), pp.2243-2254.(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518842/)

Getting started

For example if you have a bi-ventricular geometry (e.g from cardiac-geometries), you can generate fibers as follows

import cardiac_geometries
import ldrb
import dolfinx

# Generate an idealized BiV geometry
geo = cardiac_geometries.mesh.biv_ellipsoid(outdir=geodir)
# Generate fibers with 60/-60 fibers angles on the endo- and epicardium
system = ldrb.dolfinx_ldrb(mesh=geo.mesh, ffun=geo.ffun, markers=geo.markers, alpha_endo_lv=60, alpha_epi_lv=-60, beta_endo_lv=0, beta_epi_lv=0, fiber_space="P_2")
# Visualize fibers in Paraview
with dolfinx.io.VTXWriter(
    geo.mesh.comm, outdir / f"microstructure-viz.bp", [system.f0, system.s0, system.n0], engine="BP4"
) as vtx:
    vtx.write(0.0)

If you now open the file microstructure-viz.bp in Paraview (using the ADIOS2VTXReader), select Glyph and choose f0 as orientation array you can see the fiber orientations similar to the following figure (here I have also added a Calculator to compute the fiber angle) _

Note that you can achieve the the same results using the command line interface

ldrb mesh.xdmf --markers-file markers.json --fiber-space P_2 --alpha-endo-lv 60 --alpha-epi-lv -60 --beta-endo-lv 0 --beta-epi-lv 0

Note that if you don't specify a different angle for the RV or Septum, then the same angle as the LV will be used.

Installation

pip

Note that you can install the package with pip, but then you need to install FEniCSx separatly, see download

python3 -m pip install fenicsx-ldrb

Docker

To pull the latest docker image you can do

docker pull ghcr.io/finsberg/fenicsx-ldrb:main

or choose a specific version e.g

docker pull ghcr.io/finsberg/fenicsx-ldrb:v0.1.4

Note that you can also run the cli directly using docker e.g

docker run --rm -v $PWD:/home/shared -w /home/shared -it ghcr.io/finsberg/fenicsx-ldrb ldrb --help

Documentation

See https://finsberg.github.io/fenicsx-ldrb

License

MIT

Contributors

Henrik Finsberg (henriknf@simula.no)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fenicsx_ldrb-0.1.8.tar.gz (18.3 kB view details)

Uploaded Source

Built Distribution

fenicsx_ldrb-0.1.8-py3-none-any.whl (16.8 kB view details)

Uploaded Python 3

File details

Details for the file fenicsx_ldrb-0.1.8.tar.gz.

File metadata

  • Download URL: fenicsx_ldrb-0.1.8.tar.gz
  • Upload date:
  • Size: 18.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for fenicsx_ldrb-0.1.8.tar.gz
Algorithm Hash digest
SHA256 ae42a59614a55b76dbdea19f13eaf60c9dd39c9c3e8f7406a0e840938b8f8dec
MD5 895ed592cecfa81c72c106127a3f11b2
BLAKE2b-256 3f0767ade9cef63c77f9ea4e160ffd292c7534e5b9c85e381ddd5d88c56709b6

See more details on using hashes here.

Provenance

The following attestation bundles were made for fenicsx_ldrb-0.1.8.tar.gz:

Publisher: pypi.yml on finsberg/fenicsx-ldrb

Attestations:

File details

Details for the file fenicsx_ldrb-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: fenicsx_ldrb-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 16.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for fenicsx_ldrb-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 06c83bdb60b4f742b23d84c3d281871fafa55a3444c50a0830cc0448bb794886
MD5 cfec0b21524121bfe5d9f400f3f91af4
BLAKE2b-256 45a42cbee548e6636658c4388ed7b181cfdcd1e8217dccad2a4825e8a8125d42

See more details on using hashes here.

Provenance

The following attestation bundles were made for fenicsx_ldrb-0.1.8-py3-none-any.whl:

Publisher: pypi.yml on finsberg/fenicsx-ldrb

Attestations:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page